Dhaka City People information Analysis¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
In [2]:
df=pd.read_csv("Dhaka_people.csv")
df.head()
Out[2]:
Gender Age NS1 IgG IgM Area AreaType HouseType District Outcome
0 Female 45.0 0.0 0.0 0.0 Mirpur Undeveloped Building Dhaka 0.0
1 Male 17.0 0.0 0.0 1.0 Chawkbazar Developed Building Dhaka 0.0
2 Female 29.0 0.0 0.0 0.0 Paltan Undeveloped Other Dhaka 0.0
3 Female 63.0 1.0 1.0 0.0 Motijheel Developed Other Dhaka 1.0
4 Male 22.0 0.0 0.0 0.0 Gendaria Undeveloped Building Dhaka 0.0

Label_Encoder¶

In [3]:
from sklearn.preprocessing import LabelEncoder
led =LabelEncoder()
led.fit_transform(df['Gender'])
df['Gender'] =led.fit_transform(df['Gender'])
df.head()
Out[3]:
Gender Age NS1 IgG IgM Area AreaType HouseType District Outcome
0 0 45.0 0.0 0.0 0.0 Mirpur Undeveloped Building Dhaka 0.0
1 1 17.0 0.0 0.0 1.0 Chawkbazar Developed Building Dhaka 0.0
2 0 29.0 0.0 0.0 0.0 Paltan Undeveloped Other Dhaka 0.0
3 0 63.0 1.0 1.0 0.0 Motijheel Developed Other Dhaka 1.0
4 1 22.0 0.0 0.0 0.0 Gendaria Undeveloped Building Dhaka 0.0
In [4]:
df.shape
Out[4]:
(1001, 10)
In [5]:
df.isnull()
Out[5]:
Gender Age NS1 IgG IgM Area AreaType HouseType District Outcome
0 False False False False False False False False False False
1 False False False False False False False False False False
2 False False False False False False False False False False
3 False False False False False False False False False False
4 False False False False False False False False False False
... ... ... ... ... ... ... ... ... ... ...
996 False False False False False False False False False False
997 False False False False False False False False False False
998 False False False False False False False False False False
999 False False False False False False False False False False
1000 False True True True True True True True True True

1001 rows × 10 columns

In [6]:
df.isnull().sum()
Out[6]:
Gender       0
Age          1
NS1          1
IgG          1
IgM          1
Area         1
AreaType     1
HouseType    1
District     1
Outcome      1
dtype: int64
In [7]:
from sklearn.model_selection import train_test_split
train , test = train_test_split(df,test_size=.70, random_state=42)
In [8]:
train.shape
Out[8]:
(300, 10)
In [9]:
test.shape
Out[9]:
(701, 10)
In [10]:
test.head()
Out[10]:
Gender Age NS1 IgG IgM Area AreaType HouseType District Outcome
521 1 23.0 1.0 1.0 0.0 Kamrangirchar Developed Tinshed Dhaka 1.0
941 0 37.0 0.0 0.0 0.0 Rampura Developed Building Dhaka 0.0
741 1 65.0 0.0 0.0 0.0 Khilgaon Developed Tinshed Dhaka 0.0
980 1 11.0 0.0 0.0 0.0 Banasree Undeveloped Other Dhaka 0.0
411 1 24.0 0.0 0.0 1.0 Hazaribagh Developed Other Dhaka 0.0
In [11]:
test.to_csv('dhaka_testing.csv')
In [12]:
df1 =df.copy()
df2 =df.copy()
df3 =df.copy()
In [13]:
df.head()
Out[13]:
Gender Age NS1 IgG IgM Area AreaType HouseType District Outcome
0 0 45.0 0.0 0.0 0.0 Mirpur Undeveloped Building Dhaka 0.0
1 1 17.0 0.0 0.0 1.0 Chawkbazar Developed Building Dhaka 0.0
2 0 29.0 0.0 0.0 0.0 Paltan Undeveloped Other Dhaka 0.0
3 0 63.0 1.0 1.0 0.0 Motijheel Developed Other Dhaka 1.0
4 1 22.0 0.0 0.0 0.0 Gendaria Undeveloped Building Dhaka 0.0
In [14]:
df['Gender'].value_counts()
Out[14]:
0    524
1    476
2      1
Name: Gender, dtype: int64
In [15]:
Female=(524/(524+476))*100
Male =(476/(524+476))*100
print('Female {} percent of total People '.format(Female))
print('Male  {} percent of total People '.format(Male))

Female 52.400000000000006 percent of total People 
Male  47.599999999999994 percent of total People
In [16]:
sns.countplot(df['Gender'],color='#2B00FF')
Out[16]:
<Axes: ylabel='count'>

Basic Visualization using Seaborn Library¶

In [17]:
sns.countplot( y = 'Gender', hue='Age', data=df )
Out[17]:
<Axes: xlabel='count', ylabel='Gender'>
In [18]:
sns.countplot( x = 'Age', hue='Gender', data=df )
Out[18]:
<Axes: xlabel='Age', ylabel='count'>
In [19]:
sns.countplot( x = 'Gender', hue='AreaType', data=df )
Out[19]:
<Axes: xlabel='Gender', ylabel='count'>
In [20]:
sns.countplot( y = 'Area', hue='Gender', data=df )
Out[20]:
<Axes: xlabel='count', ylabel='Area'>
In [21]:
sns.countplot( x = 'HouseType', hue='Gender', data=df )
Out[21]:
<Axes: xlabel='HouseType', ylabel='count'>
In [22]:
sns.countplot( x = 'Outcome', hue='Gender', data=df )
Out[22]:
<Axes: xlabel='Outcome', ylabel='count'>
In [23]:
sns.countplot( x = 'NS1', hue='Gender', data=df )
Out[23]:
<Axes: xlabel='NS1', ylabel='count'>
In [24]:
df['AreaType'].value_counts()
Out[24]:
Developed      501
Undeveloped    499
Name: AreaType, dtype: int64
In [25]:
plt.subplots(figsize=(10,6)) 
sns.countplot(x = 'HouseType', hue='AreaType', data=df)
Out[25]:
<Axes: xlabel='HouseType', ylabel='count'>
In [26]:
plt.subplots(figsize=(10,6)) 
sns.countplot(y = 'Area', hue='AreaType', data=df)
Out[26]:
<Axes: xlabel='count', ylabel='Area'>
In [27]:
plt.subplots(figsize=(10,6)) 
sns.countplot(y= 'Area', hue='HouseType', data=df)
Out[27]:
<Axes: xlabel='count', ylabel='Area'>

Encoder of Label_encoder¶

In [28]:
from sklearn.preprocessing import LabelEncoder
label =LabelEncoder()
In [29]:
df1.columns
Out[29]:
Index(['Gender', 'Age', 'NS1', 'IgG', 'IgM', 'Area', 'AreaType', 'HouseType',
       'District', 'Outcome'],
      dtype='object')
In [30]:
from pandas.core.dtypes.common import is_numeric_dtype
for column in df1.columns:
    if is_numeric_dtype(df1[column]):
        continue
    else:
        df1[column] = label.fit_transform(df1[column])
In [31]:
df1.head()
Out[31]:
Gender Age NS1 IgG IgM Area AreaType HouseType District Outcome
0 0 45.0 0.0 0.0 0.0 22 1 0 0 0.0
1 1 17.0 0.0 0.0 1.0 7 0 0 0 0.0
2 0 29.0 0.0 0.0 0.0 27 1 1 0 0.0
3 0 63.0 1.0 1.0 0.0 24 0 1 0 1.0
4 1 22.0 0.0 0.0 0.0 10 1 0 0 0.0

Pandas Profiling & pie¶

In [32]:
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import warnings
warnings.filterwarnings('ignore')
In [33]:
df.head()
Out[33]:
Gender Age NS1 IgG IgM Area AreaType HouseType District Outcome
0 0 45.0 0.0 0.0 0.0 Mirpur Undeveloped Building Dhaka 0.0
1 1 17.0 0.0 0.0 1.0 Chawkbazar Developed Building Dhaka 0.0
2 0 29.0 0.0 0.0 0.0 Paltan Undeveloped Other Dhaka 0.0
3 0 63.0 1.0 1.0 0.0 Motijheel Developed Other Dhaka 1.0
4 1 22.0 0.0 0.0 0.0 Gendaria Undeveloped Building Dhaka 0.0
In [34]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1001 entries, 0 to 1000
Data columns (total 10 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Gender     1001 non-null   int32  
 1   Age        1000 non-null   float64
 2   NS1        1000 non-null   float64
 3   IgG        1000 non-null   float64
 4   IgM        1000 non-null   float64
 5   Area       1000 non-null   object 
 6   AreaType   1000 non-null   object 
 7   HouseType  1000 non-null   object 
 8   District   1000 non-null   object 
 9   Outcome    1000 non-null   float64
dtypes: float64(5), int32(1), object(4)
memory usage: 74.4+ KB
In [35]:
x =df.drop('Gender',axis=1)
y = df[['Gender']]
In [36]:
x.head()
Out[36]:
Age NS1 IgG IgM Area AreaType HouseType District Outcome
0 45.0 0.0 0.0 0.0 Mirpur Undeveloped Building Dhaka 0.0
1 17.0 0.0 0.0 1.0 Chawkbazar Developed Building Dhaka 0.0
2 29.0 0.0 0.0 0.0 Paltan Undeveloped Other Dhaka 0.0
3 63.0 1.0 1.0 0.0 Motijheel Developed Other Dhaka 1.0
4 22.0 0.0 0.0 0.0 Gendaria Undeveloped Building Dhaka 0.0
In [37]:
y.head()
Out[37]:
Gender
0 0
1 1
2 0
3 0
4 1
In [38]:
print('Gander  in 100%')
round(df.Gender.value_counts()*100/len(df),1)

Gander  in 100%
Out[38]:
0    52.3
1    47.6
2     0.1
Name: Gender, dtype: float64

Vaz¶

In [39]:
Gender =df['Gender'].value_counts()
transctions = Gender.index
quantity =Gender.values
figure =px.pie(df,
               values=quantity,
               names =transctions,hole=.70,
               title=" Dhaka City People Gender (Female , Male or Other ) ")

figure.show()
In [40]:
Age =df['Age'].value_counts()
transctions = Age.index
quantity =Age.values
figure =px.pie(df,
               values=quantity,
               names =transctions,hole=.70,
               title=" Dhaka City People Age ")

figure.show()
In [41]:
 Area =df['Area'].value_counts()
transctions = Area.index
quantity =Area.values
figure =px.pie(df,
               values=quantity,
               names =transctions,hole=.70,
               title="  Area in Dhaka City ")

figure.show()
In [42]:
 HouseType=df['HouseType'].value_counts()
transctions = HouseType.index
quantity =HouseType.values
figure =px.pie(df,
               values=quantity,
               names =transctions,hole=.70,
               title=" Dhaka City People Live in HouseType ")

figure.show()
In [43]:
AreaType=df['AreaType'].value_counts()
transctions =AreaType.index
quantity =AreaType.values
figure =px.pie(df,
               values=quantity,
               names =transctions,hole=.70,
               title=" Dhaka City People Live in AreaType (Developed  or Undeveloped)")

figure.show()

Pandas Profiling¶

In [44]:
import pandas as pd
from ydata_profiling import ProfileReport
ProfileReport(x, title="Dhaka_City-information_Analysis_Report")

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
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Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[44]:










    
    































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